Rapid identification of plant- and chemical-dyed cotton fabrics using the near-infrared technique

Abstract

Plant dye is a promising dyestuff to be used in textiles due to its unique environmental compatibility. However, currently there is no effective method for the identification of plant-dyed and chemical-dyed textiles. In this study, near-infrared (NIR) spectroscopy combined with three kinds of pattern recognition methods, namely soft independent modeling of class analogy (SIMCA), partial least squares (PLS) regression and principal component regression (PCR), were applied to identify cotton fabrics dyed with plant and chemical dyes. A total of 336 plant dye and chemical dye dyed cotton fabrics were prepared and the NIR spectra were collected; 267 samples were used as the calibration set, while the remaining 69 samples were used as the validation set. After pretreatment with the Savitzky–Golay first derivative, the calibration model was constructed. In the SIMCA model, the correct recognition rate values of the calibration and prediction sets were 100% and 98.55%, respectively. The PLS model showed that the number of principal components (PCs) and the correlation coefficient ( R2) were 8 and 0.9978, respectively, and the results of PCR were PC = 10, R2 = 0.9937. Both methods were satisfactory for the predicted results. The overall results indicated that NIR spectroscopy could be used for rapid and nondestructive identification of plant-dyed cotton fabrics and chemical-dyed cotton fabrics.

Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network

“…It is the best choice to achieve real-time, rapid, and nondestructive measurement [ 10 ]. Its online technology has been successfully applied in the fields of medicine, petrochemical, food, and agriculture, and textile testing is beginning to move from the laboratory stage to the industrial application stage [ 11 ]. The industrial application of the near-infrared spectroscopy analysis method for waste textiles is different from the laboratory mainly because of the diversity of recovered samples.…”

Classification of waste cotton from different countries using the near-infrared technique

“…The NIR model for waste cotton was constructed as in our previous studies 14,18 ; the spectrum quant + was used in this work and SIMCA and PLS were applied to construct the NIR model. SIMCA is a statistical method for the supervised classification of data.…”

“…Using the above methods, we have successfully predicted natural fibers, pulp and dyed cotton fabrics, in which the raw materials were all composed of cellulose. [13][14][15]18 In this study, 350 waste cottons from six different countries were collected, the NIR spectra of the samples were scanned and analyzed then NIR models were constructed for classification. SIMCA and PLS methods were used to build the models, respectively.…”

“…Using the above methods, we have successfully predicted natural fibers, pulp and dyed cotton fabrics, in which the raw materials were all composed of cellulose. 13–15,18…”

Photochemical Stability of a Cotton Fabric Surface Dyed with a Reactive Triphenodioxazine Dye

“…The NIR-CI method provides useful insights on sample components spatial distribution by a chemical image, enabling a sample chemical and/or physical heterogeneity degree evaluation. [34][35][36][37]. The obtained chemical images are visualized in the forms of threedimensional blocks of data, further implied in partial least squares-dynamic analysis (PLS-DA) with the Evince software.…”